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86 lines
3.5 KiB
86 lines
3.5 KiB
import stormpy
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import stormpy.core
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import stormpy.info
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import pycarl
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import pycarl.core
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import stormpy.examples
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import stormpy.examples.files
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import stormpy.pomdp
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import stormpy._config as config
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def example_parametric_models_01():
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# Check support for parameters
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if not config.storm_with_pars:
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print("Support parameters is missing. Try building storm-pars.")
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return
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import stormpy.pars
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from pycarl.formula import FormulaType, Relation
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if stormpy.info.storm_ratfunc_use_cln():
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import pycarl.cln.formula
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else:
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import pycarl.gmp.formula
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# Prevent curious side effects from earlier runs (for tests only)
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pycarl.clear_pools()
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# ###
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# # How to apply an unknown FSC to obtain a pMC from a POMDP
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# path = stormpy.examples.files.prism_pomdp_maze
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# prism_program = stormpy.parse_prism_program(path)
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#
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# formula_str = "P=? [!\"bad\" U \"goal\"]"
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# properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
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# # construct the POMDP
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# pomdp = stormpy.build_model(prism_program, properties)
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# # make its representation canonic.
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# pomdp = stormpy.pomdp.make_canonic(pomdp)
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# # make the POMDP simple. This step is optional but often beneficial
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# pomdp = stormpy.pomdp.make_simple(pomdp)
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# # construct the memory for the FSC
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# # in this case, a selective counter with two states
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# memory_builder = stormpy.pomdp.PomdpMemoryBuilder()
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# memory = memory_builder.build(stormpy.pomdp.PomdpMemoryPattern.selective_counter, 2)
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# # apply the memory onto the POMDP to get the cartesian product
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# pomdp = stormpy.pomdp.unfold_memory(pomdp, memory)
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# # apply the memory onto the POMDP to get the cartesian product
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# pmc = stormpy.pomdp.apply_unknown_fsc(pomdp, stormpy.pomdp.PomdpFscApplicationMode.simple_linear)
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####
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# How to apply an unknown FSC to obtain a pMC from a pPOMDP
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path = stormpy.examples.files.prism_par_pomdp_maze
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prism_program = stormpy.parse_prism_program(path)
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formula_str = "P=? [!\"bad\" U \"goal\"]"
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properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
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# construct the pPOMDP
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options = stormpy.BuilderOptions([p.raw_formula for p in properties])
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options.set_build_state_valuations()
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options.set_build_choice_labels()
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pomdp = stormpy.build_sparse_parametric_model_with_options(prism_program, options)
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# make its representation canonic.
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pomdp = stormpy.pomdp.make_canonic(pomdp)
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# construct the memory for the FSC
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# in this case, a selective counter with two states
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memory_builder = stormpy.pomdp.PomdpMemoryBuilder()
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memory = memory_builder.build(stormpy.pomdp.PomdpMemoryPattern.selective_counter, 3)
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# apply the memory onto the POMDP to get the cartesian product
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pomdp = stormpy.pomdp.unfold_memory(pomdp, memory, add_memory_labels=True, keep_state_valuations=True)
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# make the POMDP simple. This step is optional but often beneficial
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pomdp = stormpy.pomdp.make_simple(pomdp, keep_state_valuations=True)
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# apply the unknown FSC to obtain a pmc from the POMDP
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pmc = stormpy.pomdp.apply_unknown_fsc(pomdp, stormpy.pomdp.PomdpFscApplicationMode.simple_linear)
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export_pmc = False # Set to True to export the pMC as drn.
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if export_pmc:
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export_options = stormpy.core.DirectEncodingOptions()
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export_options.allow_placeholders = False
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stormpy.export_to_drn(pmc, "test.out", export_options)
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if __name__ == '__main__':
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example_parametric_models_01()
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